The conventional neutral point clamped (NPC) multilevel inverter topology needs voltage balancing circuits to balance the dc-link capacitor, the number of component count is high and output voltage is half of the input voltage which increase the size of the source side dc/dc converter in PV applications. In this paper, a new topology of seven level neutral point clamped inverter is developed. The proposed inverter has a self-voltage boosting capability using a floating capacitor to boost the output voltage one and half of the input voltage. In this proposed topology, no additional sensors are required for the floating capacitor voltage stability, which results in minimizing the complexity of designing the inverter. The direct link between the neutral point and the mid-point of the dc-link capacitors significantly reduces the leakage current and common mode voltage in this inverter. A thorough comparison between the developed topology and recent suggested topologies is carried out which set the benchmark for the proposed one due to its lower switch count and higher voltage gain. The proposed topology is verified in simulation and prototype hardware model and results are discussed with dynamic load variations. The efficiency of the inverter is 97.1% @200W and low as 88.1 % @ inductive load. The voltage THD is 17.01% for simulation and 19.3% I for experimental is results.

This paper introduces a new hybrid DC–DC converter with enhanced voltage gain and synchronized multiple output capabilities, specifically tailored for smart grid applications. The proposed converter is based on the integration of non-isolated Zeta and Mahafzah converters, comprising a single controlled switch, two diodes, three inductors, and two coupling capacitors. The primary objective of this novel hybrid converter is to improve voltage gain as compared to conventional Zeta and Mahafzah topologies. By achieving higher voltage gain at lower duty cycles, the converter effectively reduces voltage stress on semiconductor switches and output diodes, thereby enhancing overall performance and reliability. A comprehensive examination of the hybrid converter’s operating principle is presented, along with detailed calculations of duty cycle and switching losses. The paper also explores the converter’s application in smart grids, specifically in the context of renewable energy systems and electric vehicles. Two distinct scenarios are analyzed to evaluate the converter’s efficacy. Firstly, the converter is assessed as a DC–DC converter for renewable energy systems, highlighting its relevance in sustainable energy applications. Secondly, the converter is evaluated as an electric vehicle adapter, showcasing its potential in the transportation sector. To validate the converter’s performance, extensive simulations are carried out using MATLAB/SIMULINK with parameters set at 25 kW, 200 V, and 130 A. The simulation results demonstrate the converter’s ability to efficiently supply multiple loads with opposing energy flows, making it a promising technology for optimized grid management and energy distribution. Moreover, the paper investigates the total harmonic distortion (THD) of the grid current, focusing on its impact in smart grid environments. Notably, the new hybrid converter topology achieves a THD of 21.11% for the grid current, indicating its ability to effectively mitigate harmonics and improve power quality. Overall, this research introduces a cutting-edge hybrid DC–DC converter that enhances voltage gain and synchronizes multiple outputs, specifically catering to the requirements of smart grid applications. The findings underscore the converter’s potential to significantly contribute to the advancement of efficient and resilient power conversion technologies for smart grids, enabling seamless integration of renewable energy systems and electric vehicles into the grid.

This paper proposes a new hybrid flyback-Cuk (HFC) converter. The new converter consists of a single switch, a single isolated input, and dual output based on flyback and Cuk topologies. The new HFC topology is proposed to reduce switching losses and improve the duty cycle range over which voltage can be stepped down, which would ultimately lead to an increase in efficiency. For step-down capability, the traditional single topologies (flyback or Cuk) require a less than 50% duty cycle. The low duty cycle of conventional converters leads to low operational efficiency. Therefore, the developed HFC can operate at a duty cycle of up to 85% for the same capability. The analysis, derivations, design, and simulation of the proposed HFC are thoroughly discussed for two different applications at two different power levels. The simulation results are obtained using MATLAB 2020a. The developed HFC’s efficiency as a function of the duty cycle is plotted, which reaches 89%, representing a significant efficiency improvement. The proposed converter can supply and absorb power simultaneously, giving it a significant edge over other converters. It is suitable for energy conversion and storage systems, such as renewable energy systems and electric vehicles (EV). To show the effectiveness and validate the new topology proposed, an EV along with battery energy storage (BES), is applied to charge (EV) and recharge (BES) simultaneously. The simulation results of 1.5 kW of HFC-PFC over the universal voltage range show that the proposed HFC can achieve a high power factor up to 97.5% at 260 V_rms. Moreover, the total harmonics distortion is measured between 36.25 and 27.69%. Thus, the results can achieve all required functions efficiently with minimum losses at a high range of duty cycles.

Establishing real-time models for electric motors is important when capturing authentic dynamic behavior of the motors to improve control performance, enhance robustness, and support diagnosis. Quantized sensors are less expensive, and remotely controlled motors mandate signal quantization. Such limitations on observations introduce challenging issues in motor parameter estimation. This paper develops estimators for model parameters of permanent-magnet direct current (PMDC) motors using quantized speed measurements. A typical linearized model structure of PMDC motors is used as a benchmark platform to demonstrate the technology and its key properties and benefits. Convergence properties are established. Simulations and experimental studies are performed to illustrate potential applications of the technology.

Artificial intelligence (AI) techniques are widely used in fault diagnosis because they are superior in detection and prediction. The detection of faults in power systems containing electronic components is critical. The switch faults of the voltage source inverter (VSI) have a severe impact on the driving system. Short-circuit switches increase the thermal stress due to their fast and high stator currents. Additionally, open-circuit switches cause unstable motor operation. However, these issues are not sufficiently addressed or accurately predicted for VSI switch faults in the literature. Thus, this paper investigates the use of different AI classifiers for three-phase VSI fault diagnosis. Various AI methods are used, such as naïve Bayes, support vector machine (SVM), artificial neural network (ANN), and decision tree (DT) techniques. These methods are applied to a VSI-fed permanent magnet synchronous motor (PMSM) to detect the faults in the inverter switches. These methods use the drain–source voltage and PWM signals to decide whether the switch is healthy or unhealthy. In addition, they are compared in terms of their detection accuracy. In this regard, the comparative results show that the DT method has the highest accuracy as compared to other methods in the fault diagnosis process. Moreover, this paper proposes a novel and universal voltage compensation loop to compensate for the absence of the voltage portion due to the open switch fault. Thus, the driving system is assisted in operating under its normal operating conditions. The universal term is used because the proposed voltage compensation loop can be implemented in any type of inverter. To validate the results, the proposed system is implemented using two software programs, LTSPICE XVII-USA, WEKA 3.9-New Zealand.

In recent years, air pollution and climate change issues have pushed people worldwide to switch to using electric vehicles (EVs) instead of gas-driven vehicles. Unfortunately, most distribution system facilities are neither designed nor well prepared to accommodate these new types of loads, which are characterized by random and uncertain behavior. Therefore, this paper provides a comprehensive investigation of EVs’ effect on a realistic distribution system. It provides a technical evaluation and analysis of a real distribution system’s load and voltage drop in the presence of EVs under different charging strategies. In addition, this investigation presents a new methodology for managing EV loads under a dynamic response strategy in response to the distribution system’s critical hours. The proposed methodology is applied to a real distribution network, using the Monte Carlo method and the CYME program. Random driver behavior is taken into account in addition to various factors that affect EV load parameters. Overall, the results show that the distribution system is significantly affected by the addition of EV charging loads, which create a severe risk to feeder limits and voltage drop. However, a significant reduction in the impact of EVs can be achieved if a proper dynamic demand response programme is implemented. We hope that the outcomes of this investigation will provide decision-makers and planners with prior knowledge about the expected impact of using EVs and, consequently, enable them to take the proper actions needed to manage such load.